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Paul J. Atzberger

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Homepage Curriculum Vitae Δ Publications Research Summary Software Teaching Intranet Applied Mathematics Group Positions Available ?

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Publications

  1. GD-VAEs: Geometric Dynamic Variational Autoencoders For Learning Nonlinear Dynamics And Dimension Reductions, R. Lopez, P. J. Atzberger, Journal of Computational Physics, vol. 537, 114127, (2025), [arxiv][graphical abstract][full-paper][software].

  2. Transferable Foundation Models For Geometric Tasks On Point Cloud Representations: Geometric Neural Operators, B. Quackenbush, P. Atzberger, Machine Learning: Science and Technology, (2025), [arxiv][full-paper][software].

  3. Sparse L1-Autoencoders For Scientific Data Compression, M. Chung, R. Archibald, P. Atzberger, J. M. Solomon, Journal of Machine Learning for Modeling and Computing, vol. 6, iss. 4, (2025), [arxiv][full-paper].

  4. Protein Drift-Diffusion In Membranes With Non-Equilibrium Fluctuations Arising From Gradients In Concentration Or Temperature, D. Jasuja, P. J. Atzberger, PLOS Computational Biology, vol. 21, iss. 11, Dec, 1-20, (2025), [arxiv][full-paper].

  5. Magnus Exponential Integrators For Stiff Time-Varying Stochastic Systems, D. Jasuja, P. Atzberger, Physica D: Nonlinear Phenomena, vol. 484, 135034, (2025), [arxiv][full-paper].

  6. Simulation Of Stochastic Non-Equilibrium Thermal Effects Of Particle Inclusions Within Fluid Interfaces And Membranes, D. Jasuja, P. J. Atzberger, (preprint) arXiv, (2024), [arxiv].

  7. SDYN-GANs: Adversarial Learning Methods For Multistep Generative Models For General Order Stochastic Dynamics, P. Stinis, C. Daskalakis, P. Atzberger, Journal of Computational Physics, vol. 519, 113442, (2024), [arxiv][full-paper].

  8. Geometric Neural Operators (GNPs) For Data-Driven Deep Learning In Non-Euclidean Settings, B. Quackenbush, P. Atzberger, Machine Learning: Science and Technology, vol. 5, iss. 4, November, 045033, (2024), [arxiv][full-paper][software].

  9. Coarse-Grained Methods For Heterogeneous Vesicles With Phase-Separated Domains: Elastic Mechanics Of Shape Fluctuations, Plate Compression, And Channel Insertion, D. A. Rower, P. J. Atzberger, Mathematics and Computers in Simulation, vol. 209, 342-361, (2023), [arxiv][graphical abstract][full-paper].

  10. MLMOD: Machine Learning Methods For Data-Driven Modeling In LAMMPS, P. J. Atzberger, Journal of Open Source Software, vol. 8, iss. 89, 5620, (2023), [arxiv][full-paper][software].

  11. Protein Drift-Diffusion Dynamics And Phase Separation In Curved Cell Membranes And Dendritic Spines: Hybrid Discrete-Continuum Methods, P. D. Tran, T. A. Blanpied, P. J. Atzberger, Phys. Rev. E, vol. 106, October, 044402, (2022), [arxiv][full-paper].

  12. Surface Fluctuating Hydrodynamics Methods For The Drift-Diffusion Dynamics Of Particles And Microstructures Within Curved Fluid Interfaces, D. A. Rower, M. Padidar, P. J. Atzberger, Journal of Computational Physics, vol. 455, 110994, (2022), [arxiv][graphical abstract][full-paper].

  13. First-Passage Time Statistics On Surfaces Of General Shape: Surface PDE Solvers Using Generalized Moving Least Squares (GMLS), B. Gross, P. Kuberry, P. Atzberger, Journal of Computational Physics, vol. 453, 110932, (2022), [arxiv][full-paper].

  14. Variational Autoencoders For Learning Nonlinear Dynamics Of Physical Systems, R. Lopez, P. J. Atzberger, AAAI-MLPS Proceedings (peer-reviewed), (2021), [arxiv][graphical abstract][full-paper][software].

  15. Bayesian Inference Over The Stiefel Manifold Via The Givens Representation, A. A. Pourzanjani, R. M. Jiang, B. Mitchell, P. J. Atzberger, L. R. Petzold, Bayesian Analysis, vol. 16, iss. 2, 639-666, (2021), [arxiv][full-paper].

  16. Meshfree Methods On Manifolds For Hydrodynamic Flows On Curved Surfaces: A Generalized Moving Least-Squares (GMLS) Approach, B. Gross, N. Trask, P. Kuberry, P. Atzberger, Journal of Computational Physics, vol. 409, 109340, (2020), [arxiv][graphical abstract][full-paper].

  17. Topological Methods For Polymeric Materials: Characterizing The Relationship Between Polymer Entanglement And Viscoelasticity, E. Panagiotou, K. C. Millett, P. J. Atzberger, Polymers, vol. 11, iss. 3, (2019), [arxiv][full-paper].

  18. GMLS-Nets: A Framework For Learning From Unstructured Data, N. Trask, R. G. Patel, B. J. Gross, P. J. Atzberger, AAAI-MLPS Proceedings (peer-reviewed), (2019), [arxiv][full-paper][software].

  19. Stochastic Discontinuous Galerkin Methods (SDGM) Based On Fluctuation-Dissipation Balance, W. Pazner, N. Trask, P. Atzberger, Results in Applied Mathematics, vol. 4, 100068, (2019), [arxiv][full-paper].

  20. Spectral Numerical Exterior Calculus Methods For Differential Equations On Radial Manifolds, B. Gross, P. J. Atzberger, Journal of Scientific Computing, vol. 76, iss. 1, July, 145-165, (2018), [arxiv][full-paper].

  21. Hydrodynamic Flows On Curved Surfaces: Spectral Numerical Methods For Radial Manifold Shapes, B. J. Gross, P. J. Atzberger, Journal of Computational Physics, vol. 371, October, 663-689, (2018), [arxiv][full-paper].

  22. Electrostatics Of Nanoparticle–Wall Interactions Within Nanochannels: Role Of Double-Layer Structure And Ion–Ion Correlations, I. S. Sidhu, A. L. Frischknecht, P. J. Atzberger, ACS Omega, vol. 3, iss. 9, 11340-11353, (2018), [arxiv][full-paper].

  23. Fluctuating Hydrodynamic Methods For Fluid-Structure Interactions In Confined Channel Geometries, Y. Wang, H. Lei, P. J. Atzberger, Applied Mathematics and Mechanics, vol. 39, iss. 1, January, 125-152, (2018), [preprint][full-paper].

  24. Importance Of The Mathematical Foundations Of Machine Learning Methods For Scientific And Engineering Applications, P. Atzberger, SciML2018 Workshop, US Department of Energy, (two-page limit), (2018), [arxiv][full-paper].

  25. GMLS-Nets: Scientific Machine Learning Methods For Unstructured Data, N. Trask, R. G. Patel, B. J. Gross, P. J. Atzberger, NeurIPs 2019: Workshop on Machine Learning and the Physical Sciences, (four-page limit), (2018), [arxiv][full-paper][software].

  26. Forster Resonance Energy Transfer: Role Of Diffusion Of Fluorophore Orientation And Separation In Observed Shifts Of FRET Efficiency, B. Wallace, P. J. Atzberger, PLOS ONE, vol. 12, iss. 5, May, 1-22, (2017), [preprint][full-paper].

  27. Hydrodynamic Coupling Of Particle Inclusions Embedded In Curved Lipid Bilayer Membranes, J. K. Sigurdsson, P. J. Atzberger, Soft Matter, vol. 12, 6685-6707, (2016), [arxiv][full-paper].

  28. Fluctuating Hydrodynamics Methods For Dynamic Coarse-Grained Implicit-Solvent Simulations In LAMMPS, Y. Wang, J. Sigurdsson, P. Atzberger, SIAM J. Sci. Comput., vol. 38, iss. 5, December, S62-S77, (2016), [preprint][full-paper][software].

  29. Simulation Of Osmotic Swelling By The Stochastic Immersed Boundary Method, C. Wu, T. Fai, P. Atzberger, C. Peskin, SIAM J. Sci. Comput., vol. 37, iss. 4, January, B660-B688, (2015), [preprint][full-paper].

  30. Stochastic Reductions For Inertial Fluid-Structure Interactions Subject To Thermal Fluctuations, G. Tabak, P. J. Atzberger, SIAM Journal on Applied Mathematics, vol. 75, iss. 4, 1884-1914, (2015), [arxiv][full-paper].

  31. A First-Passage Kinetic Monte Carlo Method For Reaction–Drift–Diffusion Processes, A. J. Mauro, J. K. Sigurdsson, J. Shrake, P. J. Atzberger, S. A. Isaacson, Journal of Computational Physics, vol. 259, 536-567, (2014), [arxiv][full-paper].

  32. Spatially Adaptive Stochastic Methods For Fluid–Structure Interactions Subject To Thermal Fluctuations In Domains With Complex Geometries, P. Plunkett, J. Hu, C. Siefert, P. J. Atzberger, Journal of Computational Physics, vol. 277, 121-137, (2014), [arxiv][full-paper].

  33. Shape Matters In Protein Mobility Within Membranes, F. Quemeneur, J. K. Sigurdsson, M. Renner, P. J. Atzberger, P. Bassereau, D. Lacoste, Proceedings of the National Academy of Sciences (PNAS), vol. 111, iss. 14, 5083-5087, (2014), [preprint][full-paper].

  34. Hybrid Continuum-Particle Method For Fluctuating Lipid Bilayer Membranes With Diffusing Protein Inclusions, J. K. Sigurdsson, F. L. Brown, P. J. Atzberger, Journal of Computational Physics, vol. 252, 65-85, (2013), [preprint][full-paper].

  35. Dynamic Implicit-Solvent Coarse-Grained Models Of Lipid Bilayer Membranes: Fluctuating Hydrodynamics Thermostat, Y. Wang, J. K. Sigurdsson, E. Brandt, P. J. Atzberger, Phys. Rev. E, vol. 88, iss. 2, August, 023301, (2013), [arxiv][full-paper].

  36. Simulation Of Edge Facilitated Adsorption And Critical Concentration Induced Rupture Of Vesicles At a Surface, P. Plunkett, B. A. Camley, K. L. Weirich, J. Israelachvili, P. J. Atzberger, Soft Matter, vol. 9, iss. 35, 8420-8427, (2013), [journal cover][full-paper].

  37. Incorporating Shear Into Stochastic Eulerian–Lagrangian Methods For Rheological Studies Of Complex Fluids And Soft Materials, P. J. Atzberger, Physica D: Nonlinear Phenomena, vol. 265, 57-70, (2013), [arxiv][full-paper][software].

  38. Force Spectroscopy Of Complex Biopolymers With Heterogeneous Elasticity, D. Valdman, B. J. Lopez, M. T. Valentine, P. J. Atzberger, Soft Matter, vol. 9, 772-778, (2013), [preprint][full-paper].

  39. (Software) MANGO-SELM Package For Fluctuating Hydrodynamics Based Simulations In LAMMPS., P. J. Atzberger, , (2012), [full-paper][software].

  40. Spectral Analysis Methods For The Robust Measurement Of The Flexural Rigidity Of Biopolymers, D. Valdman, P. J. Atzberger, D. Yu, S. Kuei, M. T. Valentine, Biophysical Journal, vol. 102, iss. 5, December, 1144-1153, (2012), [preprint][full-paper].

  41. Influence Of Target Concentration And Background Binding On In Vitro Selection Of Affinity Reagents, J. Wang, J. F. Rudzinski, Q. Gong, H. T. Soh, P. J. Atzberger, PLOS ONE, vol. 7, iss. 8, August, 1-8, (2012), [preprint][full-paper].

  42. Stochastic Reduction Method For Biological Chemical Kinetics Using Time-Scale Separation, C. D. Pahlajani, P. J. Atzberger, M. Khammash, Journal of Theoretical Biology, vol. 272, iss. 1, 96-112, (2011), [preprint][full-paper].

  43. Stochastic Eulerian Lagrangian Methods For Fluid-Structure Interactions With Thermal Fluctuations, P. J. Atzberger, Journal of Computational Physics, vol. 230, iss. 8, April, 2821-2837, (2011), [arxiv][full-paper].

  44. Experimental Study Of The Separation Behavior Of Nanoparticles In Micro- And Nanochannels, S. P. Mariateresa, Microfluidics and Nanofluidics, vol. 10, 69-80, (2011), [preprint][full-paper].

  45. Spatially Adaptive Stochastic Multigrid Methods For Fluid-Structure Systems With Thermal Fluctuations, P. J. Atzberger, (technical report), (2010), [arxiv][technical report].

  46. Spatially Adaptive Stochastic Numerical Methods For Intrinsic Fluctuations In Reaction-Diffusion Systems, P. Atzberger, Journal of Computational Physics, vol. 229, 3474-3501, (2010), [arxiv][full-paper].

  47. Hybrid Elastic And Discrete-Particle Approach To Biomembrane Dynamics With Application To The Mobility Of Curved Integral Membrane Proteins, A. Naji, P. J. Atzberger, F. L. Brown, Phys. Rev. Lett., vol. 102, iss. 13, April, 138102, (2009), [arxiv][full-paper].

  48. Stochastic Eulerian-Lagrangian Methods For Fluid-Structure Interactions With Thermal Fluctuations And Shear Boundary Conditions, P. J. Atzberger, (technical report), (2009), [arxiv][technical report].

  49. Micromagnetic Selection Of Aptamers In Microfluidic Channels, X. Lou, J. Qian, Y. Xiao, L. Viel, A. E. Gerdon, E. T. Lagally, P. Atzberger, T. M. Tarasow, A. J. Heeger, H. T. Soh, Proceedings of the National Academy of Sciences (PNAS), vol. 106, iss. 9, 2989-2994, (2009), [preprint][full-paper].

  50. Analysis Of Selection Approaches For Aptamer Molecular Libraries, J. Rudzinski, T. Soh, P. Atzberger, (technical report), (2009), [technical report].

  51. A Microfluidic Pumping Mechanism Driven By Non-Equilibrium Osmotic Effects, P. J. Atzberger, S. Isaacson, C. S. Peskin, Physica D: Nonlinear Phenomena, vol. 238, iss. 14, 1168-1179, (2009), [preprint][full-paper].

  52. On The Foundations Of The Stochastic Immersed Boundary Method, P. R. Kramer, C. S. Peskin, P. J. Atzberger, Computer Methods in Applied Mechanics and Engineering, vol. 197, iss. 25-28, 2232-2249, (2008), [preprint][full-paper].

  53. Error Analysis Of a Stochastic Immersed Boundary Method Incorporating Thermal Fluctuations, P. J. Atzberger, P. R. Kramer, Mathematics and Computers in Simulation, vol. 79, iss. 3, 379-408, (2008), [preprint][full-paper].

  54. Theoretical Framework For Microscopic Osmotic Phenomena., P. J. Atzberger, P. R. Kramer, Phys Rev E, vol. 75, iss. 61, June, 061125, (2007), [arxiv][full-paper].

  55. A Note On The Correspondence Of An Immersed Boundary Method Incorporating Thermal Fluctuations With Stokesian-Brownian Dynamics, P. J. Atzberger, Physica D-Nonlinear Phenomena, vol. 226, iss. 2, 144-150, (2007), [preprint][full-paper].

  56. A Stochastic Immersed Boundary Method For Fluid-Structure Dynamics At Microscopic Length Scales, P. J. Atzberger, P. R. Kramer, C. S. Peskin, Journal of Computational Physics, vol. 224, iss. 2, 1255-1292, (2007), [arxiv][full-paper].

  57. Stochastic Immersed Boundary Method Incorporating Thermal Fluctuations (Brief Introduction), P. J. Atzberger, P. R. Kramer, C. S. Peskin, Proceedings of ICIAM 2007 (PAMM), vol. 7, iss. 1, 1121401-1121402, (2007), [preprint][full-paper].

  58. Velocity Correlations Of a Thermally Fluctuating Brownian Particle: A Novel Model Of The Hydrodynamic Coupling, P. J. Atzberger, Physics Letters A, vol. 351, iss. 4-5, 225-230, (2006), [preprint][full-paper].

  59. A Brownian Dynamics Model Of Kinesin In Three Dimensions Incorporating The Force-Extension Profile Of The Coiled-Coil Cargo Tether, P. J. Atzberger, C. S. Peskin, Bulletin of Mathematical Biology, vol. 68, iss. 1, 131-160, (2006), [arxiv][full-paper].


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*This material is based upon work supported by the National Science Foundation under Grant No. NSF DMS-0635535 and NSF CAREER DMS-0956210. Any opinions, findings and conclusions or recomendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).


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